Time series refers to an orderly set of data arranged in accordance with their time of occurrence. It is a statistical technique used to analyze and understand patterns, trends, and fluctuations in data over a specific period. Time series data can be collected at regular intervals, such as daily, monthly, or yearly, and is often used in various fields, including economics, finance, meteorology, and social sciences.

Time series data is organized in a sequential order, with each data point representing a specific time period. This allows analysts to examine how the data changes over time and identify any underlying patterns or trends. It is particularly useful for forecasting future values based on historical data.

Key Features of Time Series Data:
1. Time Interval: Time series data is collected at regular intervals, such as hourly, daily, or monthly.
2. Trend: Time series data often exhibits a trend, which represents the general direction or pattern of change over time. This can be an increasing, decreasing, or constant trend.
3. Seasonality: Many time series data exhibit seasonality, which refers to regular and predictable patterns that repeat at fixed intervals. For example, retail sales data may show higher sales during holiday seasons.
4. Cyclical Patterns: Time series data may also exhibit cyclical patterns, which are fluctuations that occur over a longer period, typically more than one year.
5. Randomness: Time series data can also contain random or irregular fluctuations that cannot be explained by the trend, seasonality, or cyclical patterns.

Applications of Time Series Analysis:
1. Economic Forecasting: Time series analysis is widely used to forecast economic variables, such as GDP, inflation rates, and stock prices.
2. Demand Forecasting: Businesses use time series analysis to forecast product demand, allowing them to optimize production and inventory management.
3. Weather Forecasting: Meteorologists use time series analysis to forecast weather conditions based on historical data patterns.
4. Financial Analysis: Time series analysis is used to analyze stock prices, interest rates, and other financial market variables.
5. Social Sciences: Time series analysis is applied in social sciences to study population dynamics, crime rates, disease outbreaks, and other social phenomena.

In conclusion, time series refers to an orderly set of data arranged in accordance with their time of occurrence. It is a valuable tool for analyzing and understanding patterns, trends, and fluctuations in data over time.

A time series is a sequence of data points ordered chronologically. It represents the values of a variable at different points in time. In this context, "time series" is commonly referred to as a dataset or collection of data where each data point corresponds to a specific time or period. Therefore, the correct answer is Option D.